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@radioeinsmusicbot@mastodonapp.uk
2025-06-05 18:17:13

🇺🇦 Auf radioeins läuft...
Jalen Ngonda:
🎵 Just as Long as Were Together
#NowPlaying #JalenNgonda
https://jalenngonda.bandcamp.com/track/just-as-long-as-were-together
https://open.spotify.com/track/6uAt2PE7YB5gHeCAFeD7G6

@arXiv_csRO_bot@mastoxiv.page
2025-06-04 07:46:16

Rodrigues Network for Learning Robot Actions
Jialiang Zhang, Haoran Geng, Yang You, Congyue Deng, Pieter Abbeel, Jitendra Malik, Leonidas Guibas
https://arxiv.org/abs/2506.02618

Rodrigues Network for Learning Robot Actions
Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers lack inductive biases that reflect the underlying kinematic structure of articulated systems. To this end, we propose the Neural Rodrigues Operator, a learnable generalization of the classical forward kinematics operation, designed to inject kinematics-aware inductive bias into neural computation. Building on this operator, we design the Rodrigues Network …

@arXiv_csCR_bot@mastoxiv.page
2025-06-04 07:27:39

Attention Knows Whom to Trust: Attention-based Trust Management for LLM Multi-Agent Systems
Pengfei He, Zhenwei Dai, Xianfeng Tang, Yue Xing, Hui Liu, Jingying Zeng, Qiankun Peng, Shrivats Agrawal, Samarth Varshney, Suhang Wang, Jiliang Tang, Qi He
https://arxiv.org/abs/2506.02546

Attention Knows Whom to Trust: Attention-based Trust Management for LLM Multi-Agent Systems
Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable messages. This vulnerability stems from a fundamental gap: LLM agents treat all incoming messages equally without evaluating their trustworthiness. While some existing studies approach the trustworthiness, they focus on a single type of harmfulness rather than analyze it in a holistic approach from multiple trustworthiness p…

@arXiv_eessIV_bot@mastoxiv.page
2025-06-05 07:23:24

A combined Machine Learning and Finite Element Modelling tool for the surgical planning of craniosynostosis correction
Itxasne Ant\'unez S\'aenz, Ane Alberdi Aramendi, David Dunaway, Juling Ong, Lara Deli\`ege, Amparo S\'aenz, Anita Ahmadi Birjandi, Noor UI Owase Jeelani, Silvia Schievano, Alessandro Borghi
https://a…

A combined Machine Learning and Finite Element Modelling tool for the surgical planning of craniosynostosis correction
Craniosynostosis is a medical condition that affects the growth of babies' heads, caused by an early fusion of cranial sutures. In recent decades, surgical treatments for craniosynostosis have significantly improved, leading to reduced invasiveness, faster recovery, and less blood loss. At Great Ormond Street Hospital (GOSH), the main surgical treatment for patients diagnosed with sagittal craniosynostosis (SC) is spring assisted cranioplasty (SAC). This procedure involves a 15x15 mm2 osteotomy…

@arXiv_csIT_bot@mastoxiv.page
2025-06-04 07:23:41

Joint Optimization based on Two-phase GNN in RIS- and DF-assisted MISO Systems with Fine-grained Rate Demands
Huijun Tang, Jieling Zhang, Zhidong Zhao, Huaming Wu, Hongjian Sun, Pengfei Jiao
https://arxiv.org/abs/2506.02642